If Google Search was yesterday’s high street, Generative Engines (ChatGPT Search, Google AI Overviews, Perplexity, Microsoft Copilot…) are today’s bustling plazas. Optimizing for these AI surfaces –GEO, Generative Engine Optimization– isn’t some gimmick. It’s where brand discovery, citations, and demand are already happening.
This guide distills the excellent AIO (AI Optimization) framework from me (Esteve Castells) into a GEO-first playbook you can run right now, and shows how to track your GEO rankings with LLM Pulse so you can prove impact, not just hope for it.
What is GEO?
GEO is Generative Engine Optimization, a new branch of digital marketing focused on optimizing how brands, products, and content appear in responses generated by large language models (LLMs). Unlike traditional SEO, which targets rankings in search engines like Google or Bing through links and keywords, GEO aims to increase a brand’s visibility, mentions, and citations within AI-driven answers. Its goal is to ensure that when users interact with generative engines, the brand is accurately represented, frequently referenced, and positioned as a trusted source of information.
Why GEO matters now
- Brands (especially B2B SaaS) are already seeing material traffic and sign-ups routed via AI answers and citations, with highly asymmetric visibility vs. Google. If you win the answer, you can win the click.
- Bing’s ecosystem powers multiple AI properties (ChatGPT, Copilot, Perplexity), so visibility there reverberates across the AI surface area.
The 10-Step GEO Playbook
1.- Make sure AI can crawl you
Don’t accidentally block AI bots at the CDN or in robots.txt. Unify your crawling rules and surface your sitemaps clearly. This is foundational to being cited in AI answers (and for any RAG system to pull your content).
Quick check
# robots.txt
User-agent: *
Allow: /
Sitemap: https://yourdomain.com/sitemap.xml2.- Ship server-side HTML for critical content
Most AI crawlers fetch JS but do not render it, so content that only appears client-side is effectively invisible. Favor SSR / SSG / ISR for product pages, docs, pricing, and key editorial.
3.- Proactively push indexation
Beyond sitemaps, use IndexNow / Bing URL submission to nudge indexing when you ship or update content. It shortens the “seen by AI” lag.
4.- Write in “answer-ready” formats
GEO favors content that’s deterministic, scannable, and complete:
- Use question-style H2 / H3 (“How does X work?”) with a crisp, direct answer under each heading.
- Add tables, lists, and FAQs to make relationships explicit.
- Include clear, evidence-backed statements and concrete figures where possible.
- Layer rich synonyms and related terms to widen the semantic net.
5.- Keep investing in real quality
Great UX and great content still win. AI systems lean heavily on the web for freshness (especially via Bing), so classic SEO discipline still compounds your GEO gains.
6.- Prioritize speed for crawl efficiency
Target TTFB ≤ 500ms on key templates. Faster responses mean deeper, more reliable AI crawling, fewer timeouts, and better coverage.
7.- Log-level bot monitoring
Track AI bot hits and error patterns in your logs (Botify, ELK / Kibana, or your stack of choice). Watch 404s, blocked resources, and crawl depth per bot family.
8.- Structured data
Schema remains valuable for Google Search and can help ancillary AI surfaces (e.g., commerce modules), but it doesn’t currently drive inclusion in conversational answers. Implement it for Google; don’t rely on it for GEO alone.
9.- Consider an LLMs.txt
It’s an emerging proposal to guide LLMs to key sections, feeds, and formats. It’s not a standard yet, but a lightweight draft can’t hurt.
Starter idea
# llms.txt (markdown)
# Canonical sections
- Docs: https://yourdomain.com/docs/
- Pricing: https://yourdomain.com/pricing/
- API: https://yourdomain.com/api/
- Changelog RSS: https://yourdomain.com/changelog.xml
# Extraction tips
- Prefer server-rendered pages.
- If pricing changes, see /pricing.10.- Build presence where AIs source truth
Strengthen coverage on Wikipedia, developer hubs like GitHub (if relevant), community threads (e.g., Reddit), and authoritative media. And remember: strong Bing visibility can echo into ChatGPT / Copilot / Perplexity answers.
AI Search Rank Tracking for GEO with LLM Pulse
You can’t optimize what you don’t measure. LLM Pulse tracks how your brand shows up across AI answers and citations, starting with ChatGPT, Perplexity and Google AI Overviews. It monitors key prompts over time, analyzes citations, and lets you compare visibility across models, so you can treat GEO like you’ve always treated SEO.
What to track
- Brand Visibility: % of tracked prompts where your brand is included in the AI answer.
- Citation Position in Answer: Which position are you put in when it comes to your citations?
- Share of Voice: How often your domain appears among the cited sources vs. competitors.
- Model Coverage: Presence across ChatGPT, Perplexity, Copilot, AI Overviews and others.
- Change Over Time: Week-over-week deltas after you ship SSR, fix crawling, or publish new “answer-ready” pages.
How to set it up in LLM Pulse
- Define prompt sets: Real questions buyers ask (by segment, product, use case). Tag them (e.g., “Pricing”, “Integrations”, “Security”). If not, don’t worry, we suggest it for you.
- Add competitors: Include their names / domains in your tracking scope for share-of-voice comparisons.
- Run and monitor: LLM Pulse will track answer inclusion and citations for each prompt over time.
- Route insights to roadmap: If ChatGPT cites your old docs or skips pricing, prioritize fixes to those pages / templates first.
Final words
GEO ≠ fully replacing SEO. It’s the layer on top of SEO that decides who gets named inside the answer. Ship crawlable, server-rendered, answer-ready content; push indexation; invest in trusted sources, and track it all with LLM Pulse so you can iterate with confidence.
Click here to try LLM Pulse today!